MétaCan
Menu
Back to cohort
Record W2604094564 · doi:10.1149/07711.0129ecst

Fluid Transport Properties from 3D Tomographic Images of Electrospun Carbon Electrodes for Flow Batteries

2017· article· en· W2604094564 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueECS Transactions · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of WaterlooMcGill University
FundersScience and Technology Facilities Council
KeywordsElectrospinningMaterials sciencePorosityLattice Boltzmann methodsCarbonizationElectrodeComposite materialFlow (mathematics)NanotechnologyBiomedical engineeringPolymerMechanicsScanning electron microscopeChemistry

Abstract

fetched live from OpenAlex

Three-dimensional x-ray computer tomography images were obtained of electrospun poly(acrylonitrile) electrodes for a flow battery. The materials were imaged before and after carbonization. Information about the internal morphology; local fiber size and porosity, was analyzed and provided key insights into both the electrospinning and carbonizing processes. It was found that traditional imaging techniques may not be suitable for materials generated through electrospinning as it is a highly dynamic process. The fiber size tended to vary throughout the process while the porosity was relatively constant. Viscous flow was modelled through the material using the Lattice Boltzmann Method and the 3D flow fields that resulted provided further information about the role of heterogenous features on the performance of an electrospun electrode in a flow battery. The local porosity of the material had the largest effect on the material’s flow dynamics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.491
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.237
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it